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1、DeepLearningbasedCodeProcessing&Generating李戈北京大学lige@pku.edu.cn深度学习应用的下一个突破——面向语言的处理9IncreasingdatasetsizeovertimeNumberofconnectionsperneuronovertime)10104Humans8CanadianHansardlep10WMTSports-1Mn6Catm107o97aImageNet10krxue4e6r10PublicSVHNn103Mouseere2b105CriminalsImageNetpmILSVRC2014s10un4n5
2、CHAPTER(11.0INTRODUCTIONMNISTCIFAR-10iote3c28izs10en103Fruitflyt2nes10TvsGvsFRotatedTvsCo1aCt1Irisa10D011010190019501985200020151950198520002015YearYearFigure1.8:D)eatasetsizeIsnhcraevaesingcrneaeuserdalgnretawtloyrkovseizretiomveer.tIinmteheearFlyigu1r9e001s.,1s0t:atIinstiticiaiallny,sthenumb
3、erofconnectionsbetweenneuronsinartificialnl1011studieddatasacetsu10singhundredsorthousandsofmanuallycompiledmneeatwsuoHrrekumsmewannatssl(imGiatresdonb,yhardwarecapabilities.Today,thenumberofconnectionsbets101900;Gosset,c19089;Anderson,1935;Fis1h7er,1936).Int2h0e1950sthroungehu1ro9n80ssi,stmhe
4、osptiloynaeedrsesignconsideration.Someartificialneuralnetworkshaveneari101619Octopusofbiologicallmhyin10s8piredmachinelearni1n4goftenworkedwi1t8hsmall,symntahneyticcodnnateacsteiotns,sspuecrhneuronasacat,anditisquitecommonforotherneuralnetwaslow-resolutition10b7itmapsofletters,thatweredesigned
5、toincurlowcotomhpFaurvtoeagtaisonmaalncyosctoannndectionsperneuronassmallermammalslikemice.Eventhehur11demonstrateagth1a0t6neuralnetworksw8ereabletolearnspecifickindsborfafiunndcoteiosnnso(tWhaidvreoawnexorbitantamountofconnectionsperneuron.Biologicalnol1053networksBiezeesfromWikipedia(2015).a
6、ndHoff,19(60;Rumelhartetal.,1986b).Inthe1980sand1990s,machinelearnings4Antbecamemorenst1a0tisticalinnatureandbegantoleveragelargerdatasetscontainingtensor1031.Adaptivelinearelement(WidrowandHoff,1960)ofthousandsuofe2xamplessuchastheMNISTdatase1t3(showninFig.L1e.e9ch)ofscansofe102.Neocognitron(F
7、ukushima,1980)handwrittennnumbers(LeCunetal.,1998b).Inthefirstdecadeofthe2000s,moref101121215Rou3n.dGwPoUrm-acceleratedconvolutionalnetwork(Chellapillaetal.,2006)sophisticatedodat0asetsofthissamesize,su6chast9heCIFAR-10dataset(Krizhevskyandr10
